[2603.10652] Are Video Reasoning Models Ready to Go Outside?

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.10652 (cs) [Submitted on 11 Mar 2026 (v1), last revised 14 Apr 2026 (this version, v2)] Title:Are Video Reasoning Models Ready to Go Outside? Authors:Yangfan He, Changgyu Boo, Jaehong Yoon View a PDF of the paper titled Are Video Reasoning Models Ready to Go Outside?, by Yangfan He and 2 other authors View PDF HTML (experimental) Abstract:In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such conditions, their understanding and reasoning degrade substantially, revealing a gap between clean, controlled (i.e., unperturbed) evaluation settings and real-world robustness. To address this limitation, we propose ROVA, a novel training framework that improves robustness by modeling a robustness-aware consistency reward under spatio-temporal corruptions. ROVA introduces a difficulty-aware online training strategy that prioritizes informative samples based on the model's evolving capability. Specifically, it continuously re-estimates sample difficulty via self-reflective evaluation, enabling adaptive training with a robustness-aware consistency reward. We also introduce PVRBench, a new benchmark that injects real-world perturbations into embodied video datasets to assess both accuracy and reasoning quality under realistic disturbances. We evaluate ROVA and baselines on PVRBench, UrbanVideo, and VisBench, where open-source and propriet...